The possibility of quantum computation using non-Abelian anyons has been considered for over a decade. However, the question of how to obtain and process information about what errors have occurred in order to negate their effects has not yet been considered. This is in stark contrast with quantum computation proposals for Abelian anyons, for which decoding algorithms have been tailor-made for many topological errorcorrecting codes and error models. Here, we address this issue by considering the properties of non-Abelian error correction, in general. We also choose a specific anyon model and error model to probe the problem in more detail. The anyon model is the charge submodel of DðS 3 Þ. This shares many properties with important models such asthe Fibonaccianyons, makingour methodmoregenerally applicable. Theerrormodelisa straightforward generalization of those used in the case of Abelian anyons for initial benchmarking of error correction methods. It is found that error correction is possible under a threshold value of 7% for the total probability of an error on each physical spin. This is remarkably comparable with the thresholds for Abelian models.

Quantitative micromechanical characterization of single cells and multicellular tissues or organisms is of fundamental importance to the study of cellular growth, morphogenesis, and cell-cell interactions. However, due to limited manipulation capabilities at the microscale, systems used for mechanical characterizations struggle to provide complete three-dimensional coverage of individual specimens. Here, we combine an acoustically driven manipulation device with a micro-force sensor to freely rotate biological samples and quantify mechanical properties at multiple regions of interest within a specimen. The versatility of this tool is demonstrated through the analysis of single Lilium longiflorum pollen grains, in combination with numerical simulations, and individual Caenorhabditis elegans nematodes. It reveals local variations in apparent stiffness for single specimens, providing previously inaccessible information and datasets on mechanical properties that serve as the basis for biophysical modelling and allow deeper insights into the biomechanics of these living systems.

Wireless small-scale robots hold great promise for diagnosis and treatment of certain diseases and/or pathologies in a minimally invasive way. [1] These mobile systems are engineered to

The carnivorous Venus flytrap catches prey by an ingenious snapping mechanism. Based on work over nearly 200 years, it has become generally accepted that two touches of the trap's sensory hairs within 30 s, each one generating an action potential, are required to trigger closure of the trap. We developed an electromechanical model, which, however, suggests that under certain circumstances one touch is sufficient to generate two action potentials. Using a force-sensing microrobotic system, we precisely quantified the sensoryhair deflection parameters necessary to trigger trap closure and correlated them with the elicited action potentials in vivo. Our results confirm the model's predictions, suggesting that the Venus flytrap may be adapted to a wider range of prey movements than previously assumed.

Physical forces are involved in the regulation of plant development and morphogenesis by translating mechanical stress into the modification of physiological processes, which, in turn, can affect cellular growth. Pollen tubes respond rapidly to external stimuli and provide an ideal system to study the effect of mechanical cues at the single-cell level. Here, pollen tubes were exposed to mechanical stress while monitoring the reconfiguration of their growth and recording the generated forces in real-time. We combined a lab-on-a-chip device with a microelectromechanical systems (MEMS)-based capacitive force sensor to mimic and quantify the forces that are involved in pollen tube navigation upon confronting mechanical obstacles. Several stages of obstacle avoidance were identified, including force perception, growth adjustment and penetration. We have experimentally determined the perceptive force threshold, which is the force threshold at which the pollen tube reacts to an obstacle, for Lilium longiflorum and Arabidopsis thaliana. In addition, the method we developed provides a way to calculate turgor pressure based on force and optical data. Pollen tubes sense physical barriers and actively adjust their growth behavior to overcome them. Furthermore, our system offers an ideal platform to investigate intracellular activity during force perception and growth adaption in tip growing cells.